This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python [1].

This document and PEP 257 (Docstring Conventions) were adapted from Guido’s original Python Style Guide essay, with some additions from Barry’s style guide [2].

This style guide evolves over time as additional conventions are identified and past conventions are rendered obsolete by changes in the language itself.

Many projects have their own coding style guidelines. In the event of any conflicts, such project-specific guides take precedence for that project.

One of Guido’s key insights is that code is read much more often than it is written. The guidelines provided here are intended to improve the readability of code and make it consistent across the wide spectrum of Python code. As PEP 20 says, “Readability counts”.

A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important.

But most importantly: know when to be inconsistent — sometimes the style guide just doesn’t apply. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don’t hesitate to ask!

In particular: do not break backwards compatibility just to comply with this PEP!

Some other good reasons to ignore a particular guideline:

When applying the guideline would make the code less readable, even for someone who is used to reading code that follows this PEP.

To be consistent with surrounding code that also breaks it (maybe for historic reasons) — although this is also an opportunity to clean up someone else’s mess (in true XP style).

Because the code in question predates the introduction of the guideline and there is no other reason to be modifying that code.

When the code needs to remain compatible with older versions of Python that don’t support the feature recommended by the style guide.

Continuation lines should align wrapped elements either vertically using Python’s implicit line joining inside parentheses, brackets and braces, or using a hanging indent. When using a hanging indent the following considerations should be applied; there should be no arguments on the first line and further indentation should be used to clearly distinguish itself as a continuation line.

Tabs should be used solely to remain consistent with code that is already indented with tabs.

Python 3 disallows mixing the use of tabs and spaces for indentation.

Python 2 code indented with a mixture of tabs and spaces should be converted to using spaces exclusively.

When invoking the Python 2 command line interpreter with the -t option, it issues warnings about code that illegally mixes tabs and spaces. When using -tt these warnings become errors. These options are highly recommended!

For flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters.

Limiting the required editor window width makes it possible to have several files open side-by-side, and works well when using code review tools that present the two versions in adjacent columns.

The default wrapping in most tools disrupts the visual structure of the code, making it more difficult to understand. The limits are chosen to avoid wrapping in editors with the window width set to 80, even if the tool places a marker glyph in the final column when wrapping lines. Some web based tools may not offer dynamic line wrapping at all.

Some teams strongly prefer a longer line length. For code maintained exclusively or primarily by a team that can reach agreement on this issue, it is okay to increase the nominal line length from 80 to 100 characters (effectively increasing the maximum length to 99 characters), provided that comments and docstrings are still wrapped at 72 characters.

The preferred way of wrapping long lines is by using Python’s implied line continuation inside parentheses, brackets and braces. Long lines can be broken over multiple lines by wrapping expressions in parentheses. These should be used in preference to using a backslash for line continuation.

Backslashes may still be appropriate at times. For example, long, multiple with-statements cannot use implicit continuation, so backslashes are acceptable:

with open('/path/to/some/file/you/want/to/read') as file_1, \
open('/path/to/some/file/being/written', 'w') as file_2:
file_2.write(file_1.read())

Another such case is with assert statements.

Make sure to indent the continued line appropriately. The preferred place to break around a binary operator is after the operator, not before it. Some examples:

Separate top-level function and class definitions with two blank lines.

Method definitions inside a class are separated by a single blank line.

Extra blank lines may be used (sparingly) to separate groups of related functions. Blank lines may be omitted between a bunch of related one-liners (e.g. a set of dummy implementations).

Use blank lines in functions, sparingly, to indicate logical sections.

Python accepts the control-L (i.e. ^L) form feed character as whitespace; Many tools treat these characters as page separators, so you may use them to separate pages of related sections of your file. Note, some editors and web-based code viewers may not recognize control-L as a form feed and will show another glyph in its place.

Code in the core Python distribution should always use UTF-8 (or ASCII in Python 2).

Files using ASCII (in Python 2) or UTF-8 (in Python 3) should not have an encoding declaration.

In the standard library, non-default encodings should be used only for test purposes or when a comment or docstring needs to mention an author name that contains non-ASCII characters; otherwise, using \x,\u, \U, or \N escapes is the preferred way to include non-ASCII data in string literals.

For Python 3.0 and beyond, the following policy is prescribed for the standard library (see PEP 3131): All identifiers in the Python standard library MUST use ASCII-only identifiers, and SHOULD use English words wherever feasible (in many cases, abbreviations and technical terms are used which aren’t English). In addition, string literals and comments must also be in ASCII. The only exceptions are (a) test cases testing the non-ASCII features, and (b) names of authors. Authors whose names are not based on the latin alphabet MUST provide a latin transliteration of their names.

Open source projects with a global audience are encouraged to adopt a similar policy.

Imports are always put at the top of the file, just after any module comments and docstrings, and before module globals and constants.

Imports should be grouped in the following order:

standard library imports

related third party imports

local application/library specific imports

You should put a blank line between each group of imports.

Put any relevant __all__ specification after the imports.</li>

Absolute imports are recommended, as they are usually more readable and tend to be better behaved (or at least give better error messages) if the import system is incorrectly configured (such as when a directory inside a package ends up on sys.path):

Implicit relative imports should never be used and have been removed in Python 3.</li>

When importing a class from a class-containing module, it’s usually okay to spell this:

from myclass import MyClass
from foo.bar.yourclass import YourClass

If this spelling causes local name clashes, then spell them

import myclass
import foo.bar.yourclass

and use “myclass.MyClass” and “foo.bar.yourclass.YourClass”.</li>

Wildcard imports (from import *</tt>) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn’t known in advance).
</p>

When republishing names this way, the guidelines below regarding public and internal interfaces still apply.</li> </ul> </div> </div>

If operators with different priorities are used, consider adding whitespace around the operators with the lowest priority(ies). Use your own judgment; however, never use more than one space, and always have the same amount of whitespace on both sides of a binary operator.

Comments that contradict the code are worse than no comments. Always make a priority of keeping the comments up-to-date when the code changes!

Comments should be complete sentences. If a comment is a phrase or sentence, its first word should be capitalized, unless it is an identifier that begins with a lower case letter (never alter the case of identifiers!).

If a comment is short, the period at the end can be omitted. Block comments generally consist of one or more paragraphs built out of complete sentences, and each sentence should end in a period.

You should use two spaces after a sentence-ending period.

When writing English, Strunk and White apply.

Python coders from non-English speaking countries: please write your comments in English, unless you are 120% sure that the code will never be read by people who don’t speak your language.

Block comments generally apply to some (or all) code that follows them, and are indented to the same level as that code. Each line of a block comment starts with a # and a single space (unless it is indented text inside the comment).

Paragraphs inside a block comment are separated by a line containing a single #.

Conventions for writing good documentation strings (a.k.a. “docstrings”) are immortalized in PEP 257.

Write docstrings for all public modules, functions, classes, and methods. Docstrings are not necessary for non-public methods, but you should have a comment that describes what the method does. This comment should appear after the def line.

PEP 257 describes good docstring conventions. Note that most importantly, the """ that ends a multiline docstring should be on a line by itself, and preferably preceded by a blank line, e.g.:

The naming conventions of Python’s library are a bit of a mess, so we’ll never get this completely consistent — nevertheless, here are the currently recommended naming standards. New modules and packages (including third party frameworks) should be written to these standards, but where an existing library has a different style, internal consistency is preferred.

There’s also the style of using a short unique prefix to group related names together. This is not used much in Python, but it is mentioned for completeness. For example, the os.stat() function returns a tuple whose items traditionally have names like st_mode,st_size, st_mtime and so on. (This is done to emphasize the correspondence with the fields of the POSIX system call struct, which helps programmers familiar with that.)

The X11 library uses a leading X for all its public functions. In Python, this style is generally deemed unnecessary because attribute and method names are prefixed with an object, and function names are prefixed with a module name.

In addition, the following special forms using leading or trailing underscores are recognized (these can generally be combined with any case convention):

_single_leading_underscore: weak “internal use” indicator. E.g. from M import * does not import objects whose name starts with an underscore.

single_trailing_underscore_: used by convention to avoid conflicts with Python keyword, e.g.

__double_leading_and_trailing_underscore__: “magic” objects or attributes that live in user-controlled namespaces. E.g. __init__, __import__ or __file__. Never invent such names; only use them as documented.

Modules should have short, all-lowercase names. Underscores can be used in the module name if it improves readability. Python packages should also have short, all-lowercase names, although the use of underscores is discouraged.

Since module names are mapped to file names, and some file systems are case insensitive and truncate long names, it is important that module names be chosen to be fairly short — this won’t be a problem on Unix, but it may be a problem when the code is transported to older Mac or Windows versions, or DOS.

When an extension module written in C or C++ has an accompanying Python module that provides a higher level (e.g. more object oriented) interface, the C/C++ module has a leading underscore (e.g. _socket).

(Let’s hope that these variables are meant for use inside one module only.) The conventions are about the same as those for functions.

Modules that are designed for use via from M import * should use the __all__ mechanism to prevent exporting globals, or use the older convention of prefixing such globals with an underscore (which you might want to do to indicate these globals are “module non-public”).

If a function argument’s name clashes with a reserved keyword, it is generally better to append a single trailing underscore rather than use an abbreviation or spelling corruption. Thus class_ is better than clss. (Perhaps better is to avoid such clashes by using a synonym.)

Use the function naming rules: lowercase with words separated by underscores as necessary to improve readability.

Use one leading underscore only for non-public methods and instance variables.

To avoid name clashes with subclasses, use two leading underscores to invoke Python’s name mangling rules.

Python mangles these names with the class name: if class Foo has an attribute named __a, it cannot be accessed by Foo.__a. (An insistent user could still gain access by calling Foo._Foo__a.) Generally, double leading underscores should be used only to avoid name conflicts with attributes in classes designed to be subclassed.

Always decide whether a class’s methods and instance variables (collectively: “attributes”) should be public or non-public. If in doubt, choose non-public; it’s easier to make it public later than to make a public attribute non-public.

Public attributes are those that you expect unrelated clients of your class to use, with your commitment to avoid backward incompatible changes. Non-public attributes are those that are not intended to be used by third parties; you make no guarantees that non-public attributes won’t change or even be removed.

We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work).

Another category of attributes are those that are part of the “subclass API” (often called “protected” in other languages). Some classes are designed to be inherited from, either to extend or modify aspects of the class’s behavior. When designing such a class, take care to make explicit decisions about which attributes are public, which are part of the subclass API, and which are truly only to be used by your base class.

With this in mind, here are the Pythonic guidelines:

Public attributes should have no leading underscores.

If your public attribute name collides with a reserved keyword, append a single trailing underscore to your attribute name. This is preferable to an abbreviation or corrupted spelling. (However, notwithstanding this rule, ‘cls’ is the preferred spelling for any variable or argument which is known to be a class, especially the first argument to a class method.)

Note 1: See the argument name recommendation above for class methods.</li>

For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax.

Note 1: Properties only work on new-style classes.

Note 2: Try to keep the functional behavior side-effect free, although side-effects such as caching are generally fine.

If your class is intended to be subclassed, and you have attributes that you do not want subclasses to use, consider naming them with double leading underscores and no trailing underscores. This invokes Python’s name mangling algorithm, where the name of the class is mangled into the attribute name. This helps avoid attribute name collisions should subclasses inadvertently contain attributes with the same name.

Note 1: Note that only the simple class name is used in the mangled name, so if a subclass chooses both the same class name and attribute name, you can still get name collisions.

Note 2: Name mangling can make certain uses, such as debugging and__getattr__(), less convenient. However the name mangling algorithm is well documented and easy to perform manually.

Note 3: Not everyone likes name mangling. Try to balance the need to avoid accidental name clashes with potential use by advanced callers.</li> </ul> </div> </div>

Any backwards compatibility guarantees apply only to public interfaces. Accordingly, it is important that users be able to clearly distinguish between public and internal interfaces.

Documented interfaces are considered public, unless the documentation explicitly declares them to be provisional or internal interfaces exempt from the usual backwards compatibility guarantees. All undocumented interfaces should be assumed to be internal.

To better support introspection, modules should explicitly declare the names in their public API using the __all__ attribute. Setting__all__ to an empty list indicates that the module has no public API.

Even with __all__ set appropriately, internal interfaces (packages, modules, classes, functions, attributes or other names) should still be prefixed with a single leading underscore.

An interface is also considered internal if any containing namespace (package, module or class) is considered internal.

Imported names should always be considered an implementation detail. Other modules must not rely on indirect access to such imported names unless they are an explicitly documented part of the containing module’s API, such as os.path or a package’s __init__ module that exposes functionality from submodules.

Code should be written in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Cython, Psyco, and such).

For example, do not rely on CPython’s efficient implementation of in-place string concatenation for statements in the form a += b or a = a + b. This optimization is fragile even in CPython (it only works for some types) and isn’t present at all in implementations that don’t use refcounting. In performance sensitive parts of the library, the ''.join() form should be used instead. This will ensure that concatenation occurs in linear time across various implementations.</li>

Comparisons to singletons like None should always be done withis or is not, never the equality operators.

Also, beware of writing if x when you really mean if x is not None — e.g. when testing whether a variable or argument that defaults to None was set to some other value. The other value might have a type (such as a container) that could be false in a boolean context!</li>

When implementing ordering operations with rich comparisons, it is best to implement all six operations (__eq__, __ne__,__lt__, __le__, __gt__, __ge__) rather than relying on other code to only exercise a particular comparison.

To minimize the effort involved, the functools.total_ordering() decorator provides a tool to generate missing comparison methods.

PEP 207 indicates that reflexivity rules are assumed by Python. Thus, the interpreter may swap y > x with x < y, y >= x with x <= y, and may swap the arguments of x == y and x != y. The sort() and min() operations are guaranteed to use the < operator and the max() function uses the > operator. However, it is best to implement all six operations so that confusion doesn’t arise in other contexts.</li>

Always use a def statement instead of an assignment statement that binds a lambda expression directly to a name.

Yes:

def f(x): return 2*x

No:

f = lambda x: 2*x

The first form means that the name of the resulting function object is specifically ‘f’ instead of the generic ‘’. This is more useful for tracebacks and string representations in general. The use of the assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression)</li>

Derive exceptions from Exception rather than BaseException. Direct inheritance from BaseException is reserved for exceptions where catching them is almost always the wrong thing to do.

Design exception hierarchies based on the distinctions that codecatching the exceptions is likely to need, rather than the locations where the exceptions are raised. Aim to answer the question “What went wrong?” programmatically, rather than only stating that “A problem occurred” (see PEP 3151 for an example of this lesson being learned for the builtin exception hierarchy)

Class naming conventions apply here, although you should add the suffix “Error” to your exception classes if the exception is an error. Non-error exceptions that are used for non-local flow control or other forms of signaling need no special suffix.</li>

Use exception chaining appropriately. In Python 3, “raise X from Y” should be used to indicate explicit replacement without losing the original traceback.

When deliberately replacing an inner exception (using “raise X” in Python 2 or “raise X from None” in Python 3.3+), ensure that relevant details are transferred to the new exception (such as preserving the attribute name when converting KeyError to AttributeError, or embedding the text of the original exception in the new exception message).</li>

When raising an exception in Python 2, use raise ValueError('message') instead of the older form raise ValueError, 'message'.

The latter form is not legal Python 3 syntax.

The paren-using form also means that when the exception arguments are long or include string formatting, you don’t need to use line continuation characters thanks to the containing parentheses.</li>

A bare except: clause will catch SystemExit and KeyboardInterrupt exceptions, making it harder to interrupt a program with Control-C, and can disguise other problems. If you want to catch all exceptions that signal program errors, useexcept Exception: (bare except is equivalent to except BaseException:).

A good rule of thumb is to limit use of bare ‘except’ clauses to two cases:

If the exception handler will be printing out or logging the traceback; at least the user will be aware that an error has occurred.

If the code needs to do some cleanup work, but then lets the exception propagate upwards with raise. try...finally can be a better way to handle this case.

When binding caught exceptions to a name, prefer the explicit name binding syntax added in Python 2.6:

When a resource is local to a particular section of code, use awith statement to ensure it is cleaned up promptly and reliably after use. A try/finally statement is also acceptable.

Context managers should be invoked through separate functions or methods whenever they do something other than acquire and release resources. For example:

Yes:

with conn.begin_transaction():
do_stuff_in_transaction(conn)

No:

with conn:
do_stuff_in_transaction(conn)

The latter example doesn’t provide any information to indicate that the __enter__ and __exit__ methods are doing something other than closing the connection after a transaction. Being explicit is important in this case.</li>

Use string methods instead of the string module.

String methods are always much faster and share the same API with unicode strings. Override this rule if backward compatibility with Pythons older than 2.0 is required.</li>

Use ''.startswith() and ''.endswith() instead of string slicing to check for prefixes or suffixes.

startswith() and endswith() are cleaner and less error prone. For example:

When checking if an object is a string, keep in mind that it might be a unicode string too! In Python 2, str and unicode have a common base class, basestring, so you can do:

if isinstance(obj, basestring):

Note that in Python 3, unicode and basestring no longer exist (there is only str) and a bytes object is no longer a kind of string (it is a sequence of integers instead)</li>

For sequences, (strings, lists, tuples), use the fact that empty sequences are false.

Yes: if not seq:
if seq:
No: if len(seq)
if not len(seq)

Don’t write string literals that rely on significant trailing whitespace. Such trailing whitespace is visually indistinguishable and some editors (or more recently, reindent.py) will trim them.

Don’t compare boolean values to True or False using ==.

Yes: if greeting:
No: if greeting == True:
Worse: if greeting is True:

The Python standard library will not use function annotations as that would result in a premature commitment to a particular annotation style. Instead, the annotations are left for users to discover and experiment with useful annotation styles.

It is recommended that third party experiments with annotations use an associated decorator to indicate how the annotation should be interpreted.

[str] was ambiguous as to whether it represented a list of strings or a value that could be either str or None.

The notation open(file:(str,bytes)) was used for a value that could be either bytes or str rather than a 2-tuple containing a str value followed by a bytes value.

The annotation seek(whence:int) exhibited a mix of over-specification and under-specification: int is too restrictive (anything with __index__ would be allowed) and it is not restrictive enough (only the values 0, 1, and 2 are allowed). Likewise, the annotation write(b: bytes) was also too restrictive (anything supporting the buffer protocol would be allowed).

Annotations such as read1(n: int=None) were self-contradictory since None is not an int. Annotations such assource_path(self, fullname:str) -> object were confusing about what the return type should be.

In addition to the above, annotations were inconsistent in the use of concrete types versus abstract types: int versus Integral and set/frozenset versus MutableSet/Set.

Some annotations in the abstract base classes were incorrect specifications. For example, set-to-set operations requireother to be another instance of Set rather than just anIterable.

A further issue was that annotations become part of the specification but weren’t being tested.

In most cases, the docstrings already included the type specifications and did so with greater clarity than the function annotations. In the remaining cases, the docstrings were improved once the annotations were removed.

The observed function annotations were too ad-hoc and inconsistent to work with a coherent system of automatic type checking or argument validation. Leaving these annotations in the code would have made it more difficult to make changes later so that automated utilities could be supported.